43 citations · 298 across the 21 of their papers we have counts for
21 papers
Random Subspace Mixture Models for Interpretable Anomaly Detection
Cetin Savkli, Catherine Schwartz
We present a new subspace-based method to construct probabilistic models for high-dimensional data and highlight its use in anomaly detection. The approach is based on a statistica…
Novel Edge and Density Metrics for Link Cohesion
Cetin Savkli, Catherine Schwartz, Amanda Galante +1
We present a new metric of link cohesion for measuring the strength of edges in complex, highly connected graphs. Link cohesion accounts for local small hop connections and associa…
GALILEO: A Generalized Low-Entropy Mixture Model
Cetin Savkli, Jeffrey Lin, Philip Graff +1
We present a new method of generating mixture models for data with categorical attributes. The keys to this approach are an entropy-based density metric in categorical space and an…
Bayesian Learning of Clique Tree Structure
Cetin Savkli, J. Ryan Carr, Philip Graff +1
The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the d…
SOCRATES: A System For Scalable Graph Analytics
Cetin Savkli, Ryan Carr, Matthew Chapman +2
A distributed semantic graph processing system that provides locality control, indexing, graph query, and parallel processing capabilities is presented.
Nonperturbative dynamics of scalar field theories through the Feynman-Schwinger representation
Cetin Savkli, Franz Gross, John Tjon
In this paper we present a summary of results obtained for scalar field theories using the Feynman-Schwinger (FSR) approach. Specifically, scalar QED and chi^2phi theories are cons…